The performance is also comparable to the state-of-the-art works on intelligent walking activity detection.Īhmed N, Rafiq JI, Islam MR (2020) Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model.
The proposed DCNN model outperforms these algorithms by using the m-module features individually and in unison with the p-module features. For performance comparison with the DCNN model, 4 other supervised learning algorithms have been used, namely Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Decision Tree (DT), and Gaussian Naive Bayes (GNB). With the fusion of features, the DCNN model performs with 2% more accuracy than the performance of the system using only IMU data. A 1-D Deep Convolutional Neural Network (DCNN) model is used for system performance evaluation, that prevents misclassification of walking activity with an average accuracy of 97% and maximum accuracy of 99%. This dataset is then processed to extract features for a supervised learning framework. For this purpose, a heterogeneous sensor system is implemented that acquires novel multi-modal data from low-cost leg-worn IMU sensors (m-module) and finger-tip based pulse sensors (p-module). In this paper, we consider the novel problem of distinguishing the walking activity defined as a sequence of repeated leg-swing actions from repetitive leg-swing activities in sitting state. In contrast, wearable systems on the waist recognize the bodily movement as a whole and not limb-specific movement. For instance, the identification of walking activity by wrist-worn, hand-held, or pocketed devices is often error-prone because tracking of actual leg movement is necessary for proper identification of a walk. The misclassification of human activity information by IoT-based smart, health monitoring devices raises concerns about their reliability.
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